农业工程学报2019，Vol.35Issue(19)：107-116,10.DOI:10.11975/j.issn.1002-6819.2019.19.013
基于无人机数码影像的冬小麦株高和生物量估算
Estimation of plant height and biomass of winter wheat based on UAV digital image
摘要
Abstract
Efficient and timely acquisition of height and biomass of plant is important in improving agricultural management. The purpose of this paper is to investigate the feasibility of using UAV remote sensing to obtain these data. We took winter wheat as an example and conducted a field experiment between April and June 2015 at the Xiaotangshan National Precision Agricultural Re-search Demonstration Base in Beijing. UAV imageries were taken by a drone from the field at jointing, flagging and flowering stage, respectively. We then developed a crop surface model (CSM) based on these imageries to calculate the plant height and compared the results with field measurements. The image indices extracted from the UAV imageries were used to calculate the biomass using a stepwise regression (SWR) model at each of the three growing stages, as well as the average over the three growing stages. We also compared SWR with the partial least square (PLSR) method and the random forest (RF) method. The results showed that the plant height estimated from the crop surface model agreed well with the measurements with R2=0.87, RMSE=6.45 cm and NRMSE=11.48%. The biomass model was calibrated separately for the jointing, flagging and flowering stage separately, as well as for inte-grating the three stages as one. Comparison with the measured biomass showed that R2, RMSE and NRMSE of the SWR model were 0.537 4, 0.0500 kg/m2 and 19.13% at the jointing stage, 0.606 6, 0.092 0 kg/m2 and 18.11% at the flagging stage, and 0.6324, 0.117 8 kg/m2 and 14.91% at the flowing stage, respectively. For average biomass over the three stages, R2, RMSE and NRMSE of the SWR model were 0.721 2, 0.137 2 kg·m-2 and 26.25% respectively. It was found that incorporating the plant height into the SWR model improved the biomass estimation, with its associated R2 and NRMSE increasing to 0.794 1 and 22.56% while RMSE reducing to 0.117 9 kg/m2. The SWR model is superior to the PLSR and RF model whose R2 was 0.677 4 and 0.657 10, respectively. In summary, we presented methods to estimate the height and biomass of plant based on UAV imagery and validated it against field experiment with winter wheat as the model plant.关键词
无人机/数码影像/作物表面模型/冬小麦/株高/生物量/逐步回归Key words
UAV/digital image/crop surface model/winter wheat/plant height/biomass/stepwise regression分类
农业科技引用本文复制引用
陶惠林,徐良骥,冯海宽,杨贵军,杨小冬,苗梦珂,代阳..基于无人机数码影像的冬小麦株高和生物量估算[J].农业工程学报,2019,35(19):107-116,10.基金项目
国家自然科学基金(41601346,41871333) （41601346,41871333）
